What are the hardware requirements for MUSE Books Target?

MUSE Books Target requires approximately 27.1 GB of VRAM and supports a context window of 2K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed MUSE Books Target and how large is it?

MUSE Books Target is developed by muse-bench, a model with 6.7b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate MUSE Books Target?

MUSE Books Target is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

MUSE Books Target by muse-bench — VRAM 27.1GB, 2K context

Name: MUSE Books Target
Author: muse-bench

MUSE Books Target is an open-source language model by muse-bench. Features: 6.7b LLM, VRAM: 27.1GB, Context: 2K, LLM Explorer Score: 0.15.

Arxiv:1910.09700 Endpoints compatible Llama Region:us Safetensors Sharded Tensorflow

Model Card on HF 🤗: https://huggingface.co/muse-bench/MUSE-books_target

MUSE Books Target Parameters and Internals

LLM Name	MUSE Books Target
Repository 🤗	https://huggingface.co/muse-bench/MUSE-books_target
Model Size	6.7b
Required VRAM	27.1 GB
Updated	2026-07-28
Maintainer	muse-bench
Model Type	llama
Model Files	4.8 GB: 1-of-6 4.9 GB: 2-of-6 4.9 GB: 3-of-6 4.9 GB: 4-of-6 4.9 GB: 5-of-6 2.7 GB: 6-of-6
Model Architecture	LlamaForCausalLM
Context Length	2048
Model Max Length	2048
Transformers Version	4.39.3
Vocabulary Size	32000
Torch Data Type	float32

Rank the MUSE Books Target capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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Note: green Score (e.g. "73.2") means that the model is better than muse-bench/MUSE-books_target.